Title: The Elusive X-Factor: A Critique of J. M. Kaplan’s Model of Race and IQ

Author: Dalliard

Abstract: Jonathan Michael Kaplan recently published a challenge to the hereditarian account of the IQ gap between whites and blacks in the United States (Kaplan, 2014). He argues that racism and “racialized environments” constitute race-specific “X-factors” that could plausibly cause the gap, using simulations to support this contention. I show that Kaplan’s model suffers from vagueness and implausibilities that render it an unpromising approach to explaining the gap, while his simulations are misspecified and provide no support for his model. I describe the proper methodology for testing for X-factors, and conclude that Kaplan’s X-factors would almost certainly already have been discovered if they did in fact exist. I also argue that the hereditarian position is well-supported, and, importantly, is amenable to a definitive empirical test.

Kaplan wrote " But positing, without any evidence, systematic differences in
hundreds or thousands of genes of small effects is surely no more plausible than
positing multiple environmental differences with small effects! as noted above, we do know of some environmental differences between the populations that are verifiably associated with differences in performance on IQ tests and related measures; we know of no genes that are so-associated."
Obviously he's not read Ward et al.'s replication of Rietveld's 3 alleles, nor my work on polygenic selection (https://drive.google.com/file/d/0B7hcznd...sp=sharing)

I've also a new paper coming which gives a final blow to Kaplan's silliness

I also have a few rather minor objections: 1) the dig at philosophers near the end is accurate but unnecessary and should be removed; 2) "HM" is close to "Herrnstein & Murray" and could lead to some confusion; 3) the review of what phenomena are Jensen effects could be more thorough. This is a very good paper, however.

(2014-Aug-12, 03:24:30)Philbrick Bastinado Wrote: [ -> ]I also have a few rather minor objections: 1) the dig at philosophers near the end is accurate but unnecessary and should be removed; 2) "HM" is close to "Herrnstein & Murray" and could lead to some confusion; 3) the review of what phenomena are Jensen effects could be more thorough. This is a very good paper, however.

I was typing a long reply, answering the article, but now for the moment, I wanted to react on this. Personally, I would not say HM, but better HH, for hereditarian hypothesis. Alternatively if Dalliard still sticks with HM, perhaps use "H Model" instead. But in my opinion, it's not necessarily annoying. But of course, Herrnstein & Murray was the first thing that come to my mind as well. That said, the intro and abstract say clearly what HM is.

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By the way, Dalliard, I have allowed myself to contact Kaplan himself. I think when we target someone in our articles, we should contact the targeted person and his works. That's the least we should do. I hope he will come here.

I have personally a lot of articles in preparation (all semi-finished, that's why you haven't seen me published here yet) but I plan to contact everyone i cite in my articles the authors whom/whose works are close and related to the subject of the articles. That's how I encourage everyone to do their works.

(P.S. I put both the words "whom" and "whose" because I don't know the difference between the two)

"whom" is the indirect person object, i.e. the person something was given to or some such. "Who gave the present to whom?".
"whose" is a word used to make questions referring to ownership or possession of something. "Whose car is it?".
Related "who's" is a contraction for "who is". "Who's coming over later?".

I too favor HH (hereditarian hypothesis, which is also the phrase used by the target article) over HM. But author decides.

“Kaplan claims that the leading behavioral geneticist Robert Plomin has argued in his recent publications, such as Trzaskowski et al. (2013a), that the heritability of IQ is only in the range of 40 – 60 percent. In fact, Plomin has written that heritability in adults may be 80 percent, but that it is lower in children and adolescents (Plomin & Spinath, 2004). Trzaskowski et al. (2013a) analyzed a sample of 12 - year - olds.”

“It is important to understand that it follows from Turkheimer’s laws that proposed environmental effects on IQ are also expected to be confounded by genetic influences. Accor dingly, behavioral genetic research indicates that “environmental” factors , such as measures of family environment, child rearing style, and peer relations , are under substantial genetic control (Plomin et al., 1994 ; Kendler & Baker , 2007; Vinkhuyzen et al., 2010 ) .”

I would also cite this paper. I was under the impression that this was a major paper in this area because Gottfredson always refers to it as important.

“From these examples it is clear t hat racial disparities in encounters with the police do not constitute prima facie evidence of racial bias . Even if the police never relied on the ( generally reasonably accurate ) racial stereotypes about criminal offending, racial disparities in police scrutiny w ould arise because blacks are more likely than whites to engage in suspicious and illegal activities . The same inevitably applies to private security guards singling out seemingly disproportionate numbers of blacks for scrutiny . More generally, the observed black - white differences in crime rates are predictable from black - white differences in IQ and aggressiveness (Beaver et al. , 2013), and victim surveys indicate that the high arrest and conviction rates of blacks reflect their genuinely high rates of offending (New Century Foundation , 2005). T he common belief that a racially biased criminal justice system underlies the high black crime rate is difficult to reconcile with these findings .”

Beaver et al is a weak study to cite for this. Basically, they controlled for IQ and violent history and the race variable got a p value higher than .05. Big possibility of false negative here. But sure, not controlling for g is silly and is not evidence of any bias.

A good source for the accuracy of stereotypes is this chapter, which I highly recommend.

On the topic of pro-black discrimination. Jensen and others who have reviewed the bias literature (in the 80's) have noted that generally tests are not found to be biased against blacks, and sometimes found to be biased in favor of them (i.e. they don't perform as well as predicted). I don't know if still is still the case, but it is another small bias in their favor if it works on e.g. the SAT.

“American history offers an abundance of examples of how the prosperity of white - majority neighborhoods and even entire cities was destroyed as a consequence of large numbers of blacks moving in, indicating that white s’ (and other non - black s’ ) concern s about the character of their black neighbors are far from irrational.”

This is too inflammatory and doesn't even cite anything. I guess the most obvious example of this is Detroit, but I'm not aware of any actual study of it. The census data would enable one to calculate the average IQ and SD in IQ over time. So one just needs some historical socioeconomic data (crime, income, job, education) for the same area to compare with.

“Kaplan brings up stereotype threat (Steele & Aronson , 1995) as an example of a subtle environmental influence that can have a large effect on IQ scores, arguing that his X - factors could be similar in nature. However, stereotype threat is based on a causal theory of how anxiety about confirming a stereotype about intelligence hampers performance on intelligence tests. There is thus a direct and immediate link , supported by experimental evidence, between poor test performance and the proposed causal factor — so mething that certainly cannot be said of any of Kaplan’s far - fetched propositions . Furthermore, if Ka plan’s X - factors were real, their influence on IQ would have to be , for reasons discussed later in this article, far more subtle than that of stereotype threat .”

“James Flynn has criticized researchers for assuming that racism is a magical ambient force rather than one whose possible effects are manifested through such ordinary mechanisms as poverty and poor self - esteem. Kaplan rejects this argument. Ind eed, Kaplan’s racial X - factors resemble nothing so much as magic. He presents no evidence for the hypothesis that “ racialized environments ” have an effect on IQ, and his evidence for the very existence of these environment s is very weak. Nevertheless, his model pre supposes that such environments, no matter how heterogeneous, act like magic bullets, causing large, g - linked cognitive deficits in blacks from all backgrounds while miraculously bypassing all the brain systems that mediate emotional and motivatio nal processes. Furthermore, the racial X - factors do all this in such a subtle way that no statistical signals of their presence can ever be observed, making racism a causal force completely unlike all known environmental influences on IQ scores. The essent ially occult powers that Kaplan attributes to white racism take his arguments beyond the bounds of science.”

But then again, the paper was published in a philosophy of biology journal, so in a sense it already is beyond.

“The fact that Kaplan’s proposed X - factors turn out to be very elusive upon closer inspection attests to the wisdom of Jensen’s argument about the non - existence of X - factors in general . Considering that Kaplan is an associate professor of philosophy, a nother lesson that might be drawn from his very confidently presented yet completely unsuccessful challenge to HM is that a philosophical education alone is without value in a scientific dispute . A good command of the theories , methods , and evidence pertinent to the particular area of research is necessary for making productive scientific contributions .”

I completely agree with this, having spent 2 years studying philosophy academically. Sesardic also discusses this in the introduction of his book. It is another example of the disruptive influence of ill-informed philosophers on science.

“Kaplan is also oblivious to the fact that , perhaps uniquely among all the long - running disputes in social science, a definitive empirical resolution to the black - white IQ controversy is within the reach of contemporary science . The strong causal implications of DNA - based admixture studies have been frequently discussed in the literature, and t he ability gap between blacks and whites is widely recognized as one of the most significant social problems in America (Jencks & Phillips , 1998; Pai g e & Witty , 2010; Giles , 2011) . That there nevertheless has been no rush to use genomic methods to clarify the etiology of the gap test ifies to the taboo nature of the hereditarian model .”

Probably we already have the answer, some of us that is. The people who ran the big GWAS studies presumably used multi-race US samples with SNP data. Clearly, they already have the available data to decide the issue. There are two possible outcomes: 1) HH fails the test (no correlation with admixture and IQ), in which case I shall be very surprised. Anyone sitting on this kind of evidence would publish it immediately to become a scientific hero who finally disproved the nasty racists. This has not happened.
2) HH is confirmed in the test, making it very hard to come up with an even remotely plausible alternative hypothesis. It would also end the claim that there is no direct genetic evidence for HH (actually Piffer already ended this one). The author will certainly be vilified in the media and probably face academic sanctions and lose access to his data and so on. Any scientist sitting on data like this who isn't willing to face that reaction will do nothing, which is what we have seen.

From the lack of publications of type (1), I take as strong evidence for HH.

I will give you the maximum, 5 stars, for the presentation of your article. It's so nicely presented... that's great. It's what I call perfection. Yes, literally.

Regarding the text, in general, it's a tough one, but despite its quality I have some comments and little disagreements.

Quote:Indeed, the black-white income gap is often reversed after such equating (Johnson & Neal, 1998; Nyborg & Jensen, 2001; Heckman et al., 2006), which may signal the presence of discrimination in favor of blacks.

You can also cite "Intelligence, Genes, and Success: Scientists Respond to The Bell Curve" by Devlin, Fienberg, Resnick, Roeder (1997). In chapter 9, "The Hidden Gender Restriction: The Need for Proper Controls When Testing for Racial Discrimination" (pg 200) you'll see that when controlling for age, IQ and parental SES, the BW wage difference vanishes, but that when they look at the relationship within each gender group, they see that black women are expected to have more wages than white females, while black men have less than white men.

Quote:Phillips et al. (1998) found that even after controlling for more than 30 variables related the economic, educational, cognitive, emotional, and health characteristics of parents and grandparents, about a third of the IQ gap in children remained unexplained.

It's about the chapter 4 of Black-White Test Score Gap, and the IQ test studied was PPVT-R. A vocabulary test. Perhaps precise it.

Quote:However, there is little evidence of their being overrepresented in relation to the black share of the perpetrators of crime

Shouldn't it be "them" instead of "their" ?

Quote:Specifically, the g loadings and heritability coefficients of tests have been found to be intercorrelated very highly, at close to 1.00 (Rushton & Jensen, 2010).

The R&J 2010 you cite merely cited an unpublished study by Jongeneel-Brimen & te Nijenhuis. Not published yet. te Nijenhuis gave me the draft (but in exchange I made the promise not to share it, so you have to wait for the definite version). Good article indeed, but needs more samples (Panizzon's sample can be added). In any case, I prefer to cite published study, rather than unpublished, if possible.

Quote:The principally genetic nature of g has also been confirmed in multivariate behavior genetic analyses where genetic influences on different cognitive abilities have been found to be largely common rather than ability-specific (Plomin & Spinath, 2004; Trzaskowski et al., 2013b).

The two cited studies talk about genetic correlation, if my memory is correct. Of course, genetic correlation always suggest the presence of higher-order genetic g, but it needs to be definitely confirmed through CFA modeling. Like I say, those studies you cite merely "suggest" the presence of g. If g is not modeled explicitly it can't be confirmed.

Quote:Kaplan uses Levene’s test for the equality of variances to investigate whether his simulated X-factors inflate IQ variances to a statistically significant extent.

Quote:The predominant view among psychometricians and the one that is adopted in this article is that individual differences in intelligence can be conceptualized in terms of a factor hierarchy with a third-level general factor (g), second-level broad ability factors, and first-level test-specific variation (Deary, 2012). Higher-level sources of variation exert a causal influence on the lower levels of the hierarchy.

As for causal influence, it's only an assumption of the model, not exactly what it is tested. As I usually say, causality must involve a time trend, such as with longitudinal data. For example, Panizzon (2014) confirmed the existence of genetic g at the second-order level, but no causality can be inferred. Panizzon's study can't be used against van der Maas (2006) model for example. Although I (wrongly) used to believe that before. Because the others made silly comments about that they have found causal g or something like this. Just because they say it does not mean it's true.

Quote:As it happens, the variances of IQ scores in blacks are typically smaller than those of whites. Jensen (1998, p. 353) found that black standard deviations are usually in the range of 11–14 IQ points, with a mean of 12, compared to the white standard deviation of 15 points.

In Murray's (2007, see table 2) study of W-J cognitive battery, you see that SD of blacks is greater than that of whites. Why I am saying this is because Murray consider the sample he has studied to be the most representative of the US. Apparently, most (if not all) samples neglect lowest IQ portions of blacks, reducing their SD. But not in his samples. Of course, the samples amount to "only" 1669 blacks.

See:
The magnitude and components of change in the black–white IQ difference from 1920 to 1991: A birth cohort analysis of the Woodcock–Johnson standardizations

Quote:Accordingly, when Rowe & Cleveland (1996) fitted a biometric structural equation model to math and verbal test data from a genetically informative sample of black and white children, between 36 and 74 percent of the racial test score gaps was accounted for by genetic differences in the best-fitting model.21

No problem with R&C (1996). It's just that you should have added Brody's (2003) criticism to that study. See here. It's on pages 407-408.

Quote:it has consistently been found that environmental influences on test performance are negatively or not at all associated with g loadings, whereas genetic influences are associated strongly and positively with g loadings.

I will not be so sure. As you know already, see here, the evidence from CFA modeling is not compelling. It's only when you look at MCV and factor analysis or principal component that you see g is more heritable. But CFA is confirmatory in nature, which will differ with other methods, exploratory ones. Furthermore, unlike CFA, the MCV does not use latent variable approach. That's really unfortunate. That the 2nd biggest flaw in MCV. The first, biggest one, is that you can't test g-hypothesis against non-g hypotheses. There is indeed no way to assess model fit and see if the inclusion of latent g improves your outcome.

I say that for g-h2 correlation. However, that argument also applies for g and black-white difference, or any correlations between g and still other factors. As I know, the evidence in favor of Spearman hypothesis is positive but still pretty weak, because it relies mainly on MCV. And yet, MGCFA seems unable to confirm g hypothesis as the best model, but only that the g model fits the data no better (but not worse either) than non-g models (see e.g., Dolan 2000, Dolan & Hamaker 2001). Of course, when we look at the g/non-g factor(s) decomposition in g models, it appears that the weak version of Spearman hypothesis is true, thus confirming MCV. But this is still something different than model choice, which is based on model fit indices.

For g-h2 correlation however, we get the opposite. The genetic g model seems to have the best fit, but I don't see any proof from Panizzon that g is more heritable than the latent non-g factors. So, where's the Spearman effect ? Of course, there is still the problem of heritability dependent and independent of g, as you probably remember with our little conversation with Kees Jan Kan, but if we examine merely the h2, there is no evidence for Spearman hypothesis here.

You should remove Keith, and Edwards/Oakland studies. They do not satisfy the basic requirements of MI, which require (1) pattern loadings, (2) factor loadings, (3) intercept. They have ignored testing the (3).

Quote:He could, of course, aver that his X-factors are so subtle that they would not violate

What is "aver" ?

Quote:As Wicherts et al. (2004) point out, the fact that black-white IQ differences are associated with measurement invariance while the Flynn effect is not indicates that the two phenomena are separate, and that one of them does not tell us anything about the other.

Disagree. MI, as I always said, is almost always misunderstood, probably because Wicherts and others do not describe MI very accurately. In fact, you can have violation of MI concerning FE gains at the subtest level and yet when you sum all of the subtests, you don't see test bias, because the subtests biases entirely cancel out (some biases against group1, others against group2). But in the work of Wicherts (2004) the subtest bias seems not cumulative, but cancel out instead (I reach this conclusion after my close reading, because this point is not even explicited by Wicherts). That unfortunately leads to several misleading comments on MI concerning the "role" of bias in accounting for the FE gains. If true, when "controlling" for bias, the FE gains should disappear. If not, all theories based on that assumption, such as the Brand's hypothesis or the Armstrong/Woodley rule-dependence model, all these theories should be discarded.

The best illustration you have, concerning Flynn gain, is from Must et al. (2009) "Comparability of IQ scores over time" (and Shiu 2013, who followed up the Must (2009) study). MI was violated, but the biases were not cumulative. Thus, illustrating the total irrelevance of bias to account for the Flynn effect.

Besides, if factor loading invariance is not respected, you can conclude that the differences are not directly comparable. For example, if in one group (1), the tests 1-4 load together, and form a verbal factor, and tests 5-9 load together to form a fluid reasoning factor, but in the other group (2), one or several tests, e.g., test 4 loads only on the reasoning factor, and the tests 8-9 load on reasoning as well as verbal factor, that is terrible. Because it means that tests 8-9 elicit verbal and fluid reasoning ability in group2 but elicit only fluid reasoning ability in group1. As for test 4, it means that group1 use verbal ability to resolve test 4 but that group2 use reasoning ability to resolve test 4. In case like that, the situation is just awful, and there is virtually nothing you can do about that. It's better to have intercept bias rather than factor loading or pattern loading bias.

Quote:One of the principal targets of Kaplan’s article are two studies by David Rowe and colleagues (1994, 1995).

One problem with Rowe's analysis is not, like Kaplan says, about statistical significance, but it is about measurement error. A lot of the reported correlations are close to zero, because they don't use latent variables. Is it possible that random errors also hide the race interaction ? Well, the problem is that I cannot reject that possibility. I think Rowe's need to be replicated using latent var approach. I don't remember if they have corrected for measurement errors or not.

I do not understand the " in your table 1.

Finally, I would like to say the discussion about measurement invariance is good. Lot of people who talk about it don't usually go in depth like that. Or they use very technical words and formulas that take time to understand and lot of people will not care trying to understand those formulas, even though it's not necessarily complicated (depending on what).

(2014-Aug-10, 19:33:49)Duxide Wrote: [ -> ]Kaplan wrote " But positing, without any evidence, systematic differences in
hundreds or thousands of genes of small effects is surely no more plausible than
positing multiple environmental differences with small effects! as noted above, we do know of some environmental differences between the populations that are verifiably associated with differences in performance on IQ tests and related measures; we know of no genes that are so-associated."
Obviously he's not read Ward et al.'s replication of Rietveld's 3 alleles, nor my work on polygenic selection (https://drive.google.com/file/d/0B7hcznd...sp=sharing)

Kaplan's paper was submitted in July 2013 and accepted in January 2014, so he could not have taken the latest research into account. Furthermore, the variance in IQ attributable to specific alleles remains tiny.

(2014-Aug-12, 03:24:30)Philbrick Bastinado Wrote: [ -> ]I also have a few rather minor objections: 1) the dig at philosophers near the end is accurate but unnecessary and should be removed; 2) "HM" is close to "Herrnstein & Murray" and could lead to some confusion; 3) the review of what phenomena are Jensen effects could be more thorough.

1) I think it needs to be said. It's not a nasty thing to say, just a fact. Kaplan's not the only offender by any means.

2) HM is defined at the start of the article, so I don't see how it could be confused with anything else.

3) What in particular do you think is missing from my discussion?

(2014-Aug-12, 04:39:22)menghu1001 Wrote: [ -> ]I was typing a long reply, answering the article, but now for the moment, I wanted to react on this. Personally, I would not say HM, but better HH, for hereditarian hypothesis. Alternatively if Dalliard still sticks with HM, perhaps use "H Model" instead. But in my opinion, it's not necessarily annoying. But of course, Herrnstein & Murray was the first thing that come to my mind as well. That said, the intro and abstract say clearly what HM is.

I think 'hypothesis' refers to clearly circumscribed scientific propositions, while theories and models are larger, comprising many hypotheses. What I call a model might perhaps also be called a theory, but I think that's a bit too grand in this context. I contrast 'HM' with 'Kaplan's model', and I think both should be referred to with the same term, and 'model' is the best one.

Quote:By the way, Dalliard, I have allowed myself to contact Kaplan himself. I think when we target someone in our articles, we should contact the targeted person and his works. That's the least we should do. I hope he will come here.

I got Kaplan's reply, and it was exactly what I expected it to be. I don't think a productive back-and-forth exchange is possible with someone like him.

(2014-Aug-12, 12:48:49)Emil Wrote: [ -> ]“Kaplan claims that the leading behavioral geneticist Robert Plomin has argued in his recent publications, such as Trzaskowski et al. (2013a), that the heritability of IQ is only in the range of 40 – 60 percent. In fact, Plomin has written that heritability in adults may be 80 percent, but that it is lower in children and adolescents (Plomin & Spinath, 2004). Trzaskowski et al. (2013a) analyzed a sample of 12 - year - olds.”

I think Plomin and Spinath's 2004 review that I cite is sufficient, because nothing that has been published since has challenged it. The measurement error issue is obvious, and I don't think there's a need to discuss it here.

Quote:“It is important to understand that it follows from Turkheimer’s laws that proposed environmental effects on IQ are also expected to be confounded by genetic influences. Accor dingly, behavioral genetic research indicates that “environmental” factors , such as measures of family environment, child rearing style, and peer relations , are under substantial genetic control (Plomin et al., 1994 ; Kendler & Baker , 2007; Vinkhuyzen et al., 2010 ) .”

I would also cite this paper. I was under the impression that this was a major paper in this area because Gottfredson always refers to it as important.

Quote:“From these examples it is clear t hat racial disparities in encounters with the police do not constitute prima facie evidence of racial bias . Even if the police never relied on the ( generally reasonably accurate ) racial stereotypes about criminal offending, racial disparities in police scrutiny w ould arise because blacks are more likely than whites to engage in suspicious and illegal activities . The same inevitably applies to private security guards singling out seemingly disproportionate numbers of blacks for scrutiny . More generally, the observed black - white differences in crime rates are predictable from black - white differences in IQ and aggressiveness (Beaver et al. , 2013), and victim surveys indicate that the high arrest and conviction rates of blacks reflect their genuinely high rates of offending (New Century Foundation , 2005). T he common belief that a racially biased criminal justice system underlies the high black crime rate is difficult to reconcile with these findings .”

Beaver et al is a weak study to cite for this. Basically, they controlled for IQ and violent history and the race variable got a p value higher than .05. Big possibility of false negative here. But sure, not controlling for g is silly and is not evidence of any bias.

I agree that Beaver et al. is not a strong study but it does support my point that racial differences in negative social outcomes can plausibly be explained by individual differences without recource to race-specific processes. Beaver's argument is at least as well supported as Kaplan's.

Quote:A good source for the accuracy of stereotypes is this chapter, which I highly recommend.

Quote:On the topic of pro-black discrimination. Jensen and others who have reviewed the bias literature (in the 80's) have noted that generally tests are not found to be biased against blacks, and sometimes found to be biased in favor of them (i.e. they don't perform as well as predicted). I don't know if still is still the case, but it is another small bias in their favor if it works on e.g. the SAT.

The SAT and various grad school tests continue to be biased in favor of blacks when used as predictors of performance. The best explanation for this is that it paradoxically results from the fact that the tests are internally unbiased: http://wicherts.socsci.uva.nl/borsboom2008.pdf I could mention this in the article, but I think it's a marginal issue.

Quote:[quote]“American history offers an abundance of examples of how the prosperity of white - majority neighborhoods and even entire cities was destroyed as a consequence of large numbers of blacks moving in, indicating that white s’ (and other non - black s’ ) concern s about the character of their black neighbors are far from irrational.”

This is too inflammatory and doesn't even cite anything. I guess the most obvious example of this is Detroit, but I'm not aware of any actual study of it. The census data would enable one to calculate the average IQ and SD in IQ over time. So one just needs some historical socioeconomic data (crime, income, job, education) for the same area to compare with.

White flight and the resulting urban decay are such well known phenomena in 20th century US history that I regard what I wrote as common knowledge not requiring a source citation.

Quote:[quote]“Kaplan brings up stereotype threat (Steele & Aronson , 1995) as an example of a subtle environmental influence that can have a large effect on IQ scores, arguing that his X - factors could be similar in nature. However, stereotype threat is based on a causal theory of how anxiety about confirming a stereotype about intelligence hampers performance on intelligence tests. There is thus a direct and immediate link , supported by experimental evidence, between poor test performance and the proposed causal factor — so mething that certainly cannot be said of any of Kaplan’s far - fetched propositions . Furthermore, if Ka plan’s X - factors were real, their influence on IQ would have to be , for reasons discussed later in this article, far more subtle than that of stereotype threat .”

I note later in the article that ST appears to be just a laboratory curiosity without broader importance. I think that's damning enough.

Quote:“James Flynn has criticized researchers for assuming that racism is a magical ambient force rather than one whose possible effects are manifested through such ordinary mechanisms as poverty and poor self - esteem. Kaplan rejects this argument. Ind eed, Kaplan’s racial X - factors resemble nothing so much as magic. He presents no evidence for the hypothesis that “ racialized environments ” have an effect on IQ, and his evidence for the very existence of these environment s is very weak. Nevertheless, his model pre supposes that such environments, no matter how heterogeneous, act like magic bullets, causing large, g - linked cognitive deficits in blacks from all backgrounds while miraculously bypassing all the brain systems that mediate emotional and motivatio nal processes. Furthermore, the racial X - factors do all this in such a subtle way that no statistical signals of their presence can ever be observed, making racism a causal force completely unlike all known environmental influences on IQ scores. The essent ially occult powers that Kaplan attributes to white racism take his arguments beyond the bounds of science.”

But then again, the paper was published in a philosophy of biology journal, so in a sense it already is beyond.

He's attacking a scientific model using seemingly scientific arguments. I don't think he consciously attributes magical properties to his X-factors, it's just that when you unpack his assumptions, the model looks like magic. This is true of many claims in which group differences are attributed to racism.

Quote:Indeed, the black-white income gap is often reversed after such equating (Johnson & Neal, 1998; Nyborg & Jensen, 2001; Heckman et al., 2006), which may signal the presence of discrimination in favor of blacks.

You can also cite "Intelligence, Genes, and Success: Scientists Respond to The Bell Curve" by Devlin, Fienberg, Resnick, Roeder (1997). In chapter 9, "The Hidden Gender Restriction: The Need for Proper Controls When Testing for Racial Discrimination" (pg 200) you'll see that when controlling for age, IQ and parental SES, the BW wage difference vanishes, but that when they look at the relationship within each gender group, they see that black women are expected to have more wages than white females, while black men have less than white men.

Yes, the Neal & Johnson study I cite found the same thing about sex differences. In general, depending on what kinds of individuals you are looking at (e.g., women or men, highly educated or poorly educated, low IQ or high IQ), the black-white earnings gap is either largely, fully, or more than fully explained by a few covariates that are plausibly causally prior to job performance (IQ, education, etc.). I don't think that controlling for two or three variables fully removes differences between blacks and whites, so the studies I cite are probably still comparing blacks to whites who are superior in some relevant respects. For example, I suspect that controlling for criminal background would explain much of the residual gap between black and white men, but I don't know of any studies that have done that.

As labor market differences is not the focus of my article, I don't think it makes sense for me to discuss this issue in more detail. I am not trying to explain all the black-white labor market differences. I have a more limited goal which is just to show that the evidence Kaplan presents in favor of labor market discrimination is weak and subject to alternative explanations, and that you could easily make the case that whites rather than blacks are being discriminated against.

Quote:

Quote:Phillips et al. (1998) found that even after controlling for more than 30 variables related the economic, educational, cognitive, emotional, and health characteristics of parents and grandparents, about a third of the IQ gap in children remained unexplained.

It's about the chapter 4 of Black-White Test Score Gap, and the IQ test studied was PPVT-R. A vocabulary test. Perhaps precise it.

OK.

Quote:

Quote:However, there is little evidence of their being overrepresented in relation to the black share of the perpetrators of crime

Shouldn't it be "them" instead of "their" ?

Both are correct.

Quote:

Quote:Specifically, the g loadings and heritability coefficients of tests have been found to be intercorrelated very highly, at close to 1.00 (Rushton & Jensen, 2010).

The R&J 2010 you cite merely cited an unpublished study by Jongeneel-Brimen & te Nijenhuis. Not published yet. te Nijenhuis gave me the draft (but in exchange I made the promise not to share it, so you have to wait for the definite version). Good article indeed, but needs more samples (Panizzon's sample can be added). In any case, I prefer to cite published study, rather than unpublished, if possible.

What published study would you suggest? R&J 2010 is a sort of review article, so I think it's appropriate.

Quote:

Quote:The principally genetic nature of g has also been confirmed in multivariate behavior genetic analyses where genetic influences on different cognitive abilities have been found to be largely common rather than ability-specific (Plomin & Spinath, 2004; Trzaskowski et al., 2013b).

The two cited studies talk about genetic correlation, if my memory is correct. Of course, genetic correlation always suggest the presence of higher-order genetic g, but it needs to be definitely confirmed through CFA modeling. Like I say, those studies you cite merely "suggest" the presence of g. If g is not modeled explicitly it can't be confirmed.

I disagree. A genetic correlation indicates that two phenotypes share genetic influences. There is no need for a higher-order factor model to confirm this. Of course, genetic correlations can be spurious (e.g., they may not reflect shared causal influences but common ancestry) but as they have been confirmed for g in multivariate GCTA with unrelated individuals, too, that's extremely unlikely.

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Quote:Kaplan uses Levene’s test for the equality of variances to investigate whether his simulated X-factors inflate IQ variances to a statistically significant extent.

To be sure, Levene's test is flawed. See here.

I could add a note about that, although it makes no difference to my argument.

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Quote:The predominant view among psychometricians and the one that is adopted in this article is that individual differences in intelligence can be conceptualized in terms of a factor hierarchy with a third-level general factor (g), second-level broad ability factors, and first-level test-specific variation (Deary, 2012). Higher-level sources of variation exert a causal influence on the lower levels of the hierarchy.

As for causal influence, it's only an assumption of the model, not exactly what it is tested. As I usually say, causality must involve a time trend, such as with longitudinal data. For example, Panizzon (2014) confirmed the existence of genetic g at the second-order level, but no causality can be inferred. Panizzon's study can't be used against van der Maas (2006) model for example. Although I (wrongly) used to believe that before. Because the others made silly comments about that they have found causal g or something like this. Just because they say it does not mean it's true.

The causal g model is a theoretical position that undergirds much of my argument. A higher-order factor model makes no sense if you don't think g is causal, for example. This is my theoretical stance, and that of many others, and I don't see why I should spend time justifying it, or write in a theoretically non-committal language. I acknowledge that other models are possible (see footnote 13), but I favor the g model.

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Quote:As it happens, the variances of IQ scores in blacks are typically smaller than those of whites. Jensen (1998, p. 353) found that black standard deviations are usually in the range of 11–14 IQ points, with a mean of 12, compared to the white standard deviation of 15 points.

In Murray's (2007, see table 2) study of W-J cognitive battery, you see that SD of blacks is greater than that of whites. Why I am saying this is because Murray consider the sample he has studied to be the most representative of the US. Apparently, most (if not all) samples neglect lowest IQ portions of blacks, reducing their SD. But not in his samples. Of course, the samples amount to "only" 1669 blacks.

Jensen's claim is based on a wider range of tests, so it's probably more credible, although I agree that it's possible that many studies, especially older ones, either do not sample the low end of blacks, or have floor effects. I could add a footnote about this.

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Quote:Accordingly, when Rowe & Cleveland (1996) fitted a biometric structural equation model to math and verbal test data from a genetically informative sample of black and white children, between 36 and 74 percent of the racial test score gaps was accounted for by genetic differences in the best-fitting model.21

No problem with R&C (1996). It's just that you should have added Brody's (2003) criticism to that study. See here. It's on pages 407-408.

Yeah, I think that study is more interesting for its methods than its results because of sampling issues. It should be replicated now that better data are available. I will remove the reference because it's really a rather equivocal study.

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Quote:it has consistently been found that environmental influences on test performance are negatively or not at all associated with g loadings, whereas genetic influences are associated strongly and positively with g loadings.

I will not be so sure. As you know already, see here, the evidence from CFA modeling is not compelling. It's only when you look at MCV and factor analysis or principal component that you see g is more heritable. But CFA is confirmatory in nature, which will differ with other methods, exploratory ones. Furthermore, unlike CFA, the MCV does not use latent variable approach. That's really unfortunate. That the 2nd biggest flaw in MCV. The first, biggest one, is that you can't test g-hypothesis against non-g hypotheses. There is indeed no way to assess model fit and see if the inclusion of latent g improves your outcome.

I say that for g-h2 correlation. However, that argument also applies for g and black-white difference, or any correlations between g and still other factors. As I know, the evidence in favor of Spearman hypothesis is positive but still pretty weak, because it relies mainly on MCV. And yet, MGCFA seems unable to confirm g hypothesis as the best model, but only that the g model fits the data no better (but not worse either) than non-g models (see e.g., Dolan 2000, Dolan & Hamaker 2001). Of course, when we look at the g/non-g factor(s) decomposition in g models, it appears that the weak version of Spearman hypothesis is true, thus confirming MCV. But this is still something different than model choice, which is based on model fit indices.

For g-h2 correlation however, we get the opposite. The genetic g model seems to have the best fit, but I don't see any proof from Panizzon that g is more heritable than the latent non-g factors. So, where's the Spearman effect ? Of course, there is still the problem of heritability dependent and independent of g, as you probably remember with our little conversation with Kees Jan Kan, but if we examine merely the h2, there is no evidence for Spearman hypothesis here.

As I said above, the article is based on the theoretical conviction that the g model is correct. The fact that g and non-g models may fit data equally well in CFA is immaterial, because the evidence for the superiority of the g model comes not only from CFA but from studies that are independent of CFA. For example, the various Jensen and anti-Jensen effects can be readily explained by the g model, but not by non-g models.

As I explained in that exchange with Kan, the h2xg correlation is due to the fact that g explains by far the largest portion of genetic variance in any test battery. Even if some non-g factor had 100% heritability, this would not invalidate the fact that g is by far the largest source of genetic variance. For example, in Figure 2a in my article, specific genetic effects on Ability 1, if there are any, explain variance in only four tests (1-4). In contrast, genetic effects on g explain variance in all 16 tests.

You should remove Keith, and Edwards/Oakland studies. They do not satisfy the basic requirements of MI, which require (1) pattern loadings, (2) factor loadings, (3) intercept. They have ignored testing the (3).

MI comprises many stages, and it's still an analysis of MI even if not all stages are tested. But I can remove those two if you insist on it.

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Quote:He could, of course, aver that his X-factors are so subtle that they would not violate

What is "aver" ?

<i>aver verb \ə-ˈvər\
: to say (something) in a very strong and definite way</i>

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Quote:As Wicherts et al. (2004) point out, the fact that black-white IQ differences are associated with measurement invariance while the Flynn effect is not indicates that the two phenomena are separate, and that one of them does not tell us anything about the other.

Disagree. MI, as I always said, is almost always misunderstood, probably because Wicherts and others do not describe MI very accurately. In fact, you can have violation of MI concerning FE gains at the subtest level and yet when you sum all of the subtests, you don't see test bias, because the subtests biases entirely cancel out (some biases against group1, others against group2). But in the work of Wicherts (2004) the subtest bias seems not cumulative, but cancel out instead (I reach this conclusion after my close reading, because this point is not even explicited by Wicherts). That unfortunately leads to several misleading comments on MI concerning the "role" of bias in accounting for the FE gains. If true, when "controlling" for bias, the FE gains should disappear. If not, all theories based on that assumption, such as the Brand's hypothesis or the Armstrong/Woodley rule-dependence model, all these theories should be discarded.

The best illustration you have, concerning Flynn gain, is from Must et al. (2009) "Comparability of IQ scores over time" (and Shiu 2013, who followed up the Must (2009) study). MI was violated, but the biases were not cumulative. Thus, illustrating the total irrelevance of bias to account for the Flynn effect.

Besides, if factor loading invariance is not respected, you can conclude that the differences are not directly comparable. For example, if in one group (1), the tests 1-4 load together, and form a verbal factor, and tests 5-9 load together to form a fluid reasoning factor, but in the other group (2), one or several tests, e.g., test 4 loads only on the reasoning factor, and the tests 8-9 load on reasoning as well as verbal factor, that is terrible. Because it means that tests 8-9 elicit verbal and fluid reasoning ability in group2 but elicit only fluid reasoning ability in group1. As for test 4, it means that group1 use verbal ability to resolve test 4 but that group2 use reasoning ability to resolve test 4. In case like that, the situation is just awful, and there is virtually nothing you can do about that. It's better to have intercept bias rather than factor loading or pattern loading bias.

You will have explain this in more detail. When Wicherts et al. say that the b-w gap and the Flynn effect have nothing to do with each other, I take them to mean that because of measurement non-invariance cohort differences cannot be attributed to latent factors, whereas the b-w gap can be attributed to latent factors, given that MI is not violated. Do you disagree with this?

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Quote:One of the principal targets of Kaplan’s article are two studies by David Rowe and colleagues (1994, 1995).

One problem with Rowe's analysis is not, like Kaplan says, about statistical significance, but it is about measurement error. A lot of the reported correlations are close to zero, because they don't use latent variables. Is it possible that random errors also hide the race interaction ? Well, the problem is that I cannot reject that possibility. I think Rowe's need to be replicated using latent var approach. I don't remember if they have corrected for measurement errors or not.

I agree that Rowe's method is less sensitive than the latent factor approach, but I don't think measurement errors have much to do with it.